Modeling ratings of in-vehicle alerts to pedestrian by leveraging field operational tests data in a controlled laboratory study |
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| Institution: | 1. Autoliv Development AB, SE44783 Vårgårda, Sweden;2. Naval Postgraduate School, 1 University Circle, Monterey, CA 93943, USA;1. Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310028, China;2. Department of Systems Design Engineering, University of Waterloo, Waterloo N2L 3G1, Canada;3. Koguan Law School, Shanghai Jiao Tong University, 200030 Shanghai, China;4. Department of Psychology, Carnegie Mellon University, Pittsburgh 15213, United States;5. The National Key Laboratory of Human Factors, Hangzhou 310028, China;1. Department of Building Construction Management, Purdue University, West Lafayette, IN 47907, USA;2. School of Civil Engineering and Architecture, Wuhan Polytechnic University, Wuhan 430023, China;3. Office of Research and Development, Indiana Department of Transportation, West Lafayette, IN 47906, USA;4. School of Materials Science and Engineering, Chang''an University, Xi''an 710064, China;1. Department of Movement and Sports Sciences, Ghent University, Watersportlaan 2, 9000 Ghent, Belgium;2. Department of Human Movement Sciences, Vrije Universiteit Amsterdam, MOVE Research Institute Amsterdam, Van der Boechorststraat 9, NL-1081 BT Amsterdam, The Netherlands |
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| Abstract: | We show how to leverage expensive field operational tests (FOT) data in a controlled laboratory study when defining an in-vehicle algorithm that alerts drivers to pedestrians. We used an empirical approach that quantifies the relative level with which drivers are likely to accept alerts to pedestrians. The approach was used in two studies to investigate a range of contextual factors known to influence driver ratings of alerts to pedestrians issued by a driver-assistance system. Regression analysis shows that four factors consisting of combinations of pedestrian location and motion relative to the road ahead of the vehicle explain 85% of the variability in drivers’ ratings of alerts. Adding two factors related to the uncertainty of the pedestrians’ future path improves the model slightly. These findings suggest that drivers’ assessment of the danger associated with pedestrians derives largely from the possibility that they might move into the vehicle’s path, even when the vehicle is not on a collision course with the pedestrians. The less probable such an event seems, the less accepted an alert will be. Time to arrival (TTA) improved the regression model only when restricted to pedestrians in clear need of an alert, but was also found to have an effect in alert timing. This finding suggests that four contextual factors largely define the perceptual cues that drivers use to rate alerts to pedestrians. |
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| Keywords: | Pedestrian safety Driver behavior Knowledge elicitation Intelligent vehicle systems Vehicle automation |
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